Open data is shaking up civic life in eastern Europe

Curated on Posted on by Stefaan Verhulst

Anna Sienicka in the Financial Times: “I often imagine how different the world would look if citizens and social activists were able to fully understand and use data, and new technologies. Unfortunately, the entry point to this world is often inaccessible for most civil society groups…

The concept of open data has revolutionised thinking about citizens’ participation in civic life. Since the fall of communism, citizens across central and eastern Europe have been fighting for more transparent and responsive governments, and to improve collaboration between civil society and the public sector. When an institution makes its data public, it is a sign that it is committed to being transparent and accountable. A few cities have opened up data about budget spending, for example, but these remain the exception rather than the rule. Open data provides citizens with a tool to directly engage in civic life. For example, they can analyse public expenses to check how their taxes are used, track their MP’s votes or monitor the legislative process….

One of the successful projects in Ukraine is the Open School app, which provides reviews and ratings of secondary schools based on indicators such as the number of pupils who go on to university, school subject specialisations and accessibility. It allows students and parents to make informed decisions about their educational path… Another example comes from the Serbian city of Pancevo, where a maths teacher and a tax inspector have worked together to help people navigate the tax system. The idea is simple: the more people know about taxes, the less likely they are to unconsciously violate the law. Open Taxes is a free, web-based, interactive guide to key national and local taxes…(More)”

Our laws don’t do enough to protect our health data

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “A particularly sensitive type of big data is medical big data. Medical big data can consist of electronic health records, insurance claims, information entered by patients into websites such as PatientsLikeMeand more. Health information can even be gleaned from web searches, Facebook and your recent purchases.

Such data can be used for beneficial purposes by medical researchers, public health authorities, and healthcare administrators. For example, they can use it to study medical treatments, combat epidemics and reduce costs. But others who can obtain medical big data may have more selfish agendas.

I am a professor of law and bioethics who has researched big data extensively. Last year, I published a book entitled Electronic Health Records and Medical Big Data: Law and Policy.

I have become increasingly concerned about how medical big data might be used and who could use it. Our laws currently don’t do enough to prevent harm associated with big data.

What your data says about you

Personal health information could be of interest to many, including employers, financial institutions, marketers and educational institutions. Such entities may wish to exploit it for decision-making purposes.

For example, employers presumably prefer healthy employees who are productive, take few sick days and have low medical costs. However, there are laws that prohibit employers from discriminating against workers because of their health conditions. These laws are the Americans with Disabilities Act (ADA) and the Genetic Information Nondiscrimination Act. So, employers are not permitted to reject qualified applicants simply because they have diabetes, depression or a genetic abnormality.

However, the same is not true for most predictive information regarding possible future ailments. Nothing prevents employers from rejecting or firing healthy workers out of the concern that they will later develop an impairment or disability, unless that concern is based on genetic information.

What non-genetic data can provide evidence regarding future health problems? Smoking status, eating preferences, exercise habits, weight and exposure to toxins are all informative. Scientists believe that biomarkers in your blood and other health details can predict cognitive decline, depression and diabetes.

Even bicycle purchases, credit scores and voting in midterm elections can be indicators of your health status.

Gathering data

How might employers obtain predictive data? An easy source is social media, where many individuals publicly post very private information. Through social media, your employer might learn that you smoke, hate to exercise or have high cholesterol.

Another potential source is wellness programs. These programs seek to improve workers’ health through incentives to exercise, stop smoking, manage diabetes, obtain health screenings and so on. While many wellness programs are run by third party vendors that promise confidentiality, that is not always the case.

In addition, employers may be able to purchase information from data brokers that collect, compile and sell personal information. Data brokers mine sources such as social media, personal websites, U.S. Census records, state hospital records, retailers’ purchasing records, real property records, insurance claims and more. Two well-known data brokers are Spokeo and Acxiom.

Some of the data employers can obtain identify individuals by name. But even information that does not provide obvious identifying details can be valuable. Wellness program vendors, for example, might provide employers with summary data about their workforce but strip away particulars such as names and birthdates. Nevertheless, de-identified information can sometimes be re-identified by experts. Data miners can match information to data that is publicly available….(More)”.

How people update beliefs about climate change: good news and bad news

Curated on Posted on by Stefaan Verhulst

Paper by Cass R. Sunstein, Sebastian Bobadilla-Suarez, Stephanie C. Lazzaro & Tali Sharot: “People are frequently exposed to competing evidence about climate change. We examined how new information alters people’s beliefs. We find that people who are not sure that man-made climate change is occurring, and who do not favor an international agreement to reduce greenhouse gas emissions, show a form of asymmetrical updating: They change their beliefs in response to unexpected good news (suggesting that average temperature rise is likely to be less than previously thought) and fail to change their beliefs in response to unexpected bad news (suggesting that average temperature rise is likely to be greater than previously thought). By contrast, people who strongly believe that manmade climate change is occurring, and who favor an international agreement, show the opposite asymmetry: They change their beliefs far more in response to unexpected bad news (suggesting that average temperature rise is likely to be greater than previously thought) than in response to unexpected good news (suggesting that average temperature rise is likely to be smaller than previously thought). The results suggest that exposure to varied scientific evidence about climate change may increase polarization within a population due to asymmetrical updating. We explore the implications of our findings for how people will update their beliefs upon receiving new evidence about climate change, and also for other beliefs relevant to politics and law….(More)”.

The Challenges of Prediction: Lessons from Criminal Justice

Curated on Posted on by Stefaan Verhulst

Paper by David G. Robinson: “Government authorities at all levels increasingly rely on automated predictions, grounded in statistical patterns, to shape people’s lives. Software that wields government power deserves special attention, particularly when it uses historical data to decide automatically what ought to happen next.

In this article, I draw examples primarily from the domain of criminal justice — and in particular, the intersection of civil rights and criminal justice — to illustrate three structural challenges that can arise whenever law or public policy contemplates adopting predictive analytics as a tool:

1) What matters versus what the data measure;
2) Current goals versus historical patterns; and
3) Public authority versus private expertise.

After explaining each of these challenges and illustrating each with concrete examples, I describe feasible ways to avoid these problems and to do prediction more successfully…(More)”

Tech’s fight for the upper hand on open data

Curated on Posted on by Stefaan Verhulst

Rana Foroohar at the Financial Times: “One thing that’s becoming very clear to me as I report on the digital economy is that a rethink of the legal framework in which business has been conducted for many decades is going to be required. Many of the key laws that govern digital commerce (which, increasingly, is most commerce) were crafted in the 1980s or 1990s, when the internet was an entirely different place. Consider, for example, the US Computer Fraud and Abuse Act.

This 1986 law made it a federal crime to engage in “unauthorised access” to a computer connected to the internet. It was designed to prevent hackers from breaking into government or corporate systems. …While few hackers seem to have been deterred by it, the law is being used in turf battles between companies looking to monetise the most valuable commodity on the planet — your personal data. Case in point: LinkedIn vs HiQ, which may well become a groundbreaker in Silicon Valley.

LinkedIn is the dominant professional networking platform, a Facebook for corporate types. HiQ is a “data-scraping” company, one that accesses publicly available data from LinkedIn profiles and then mixes it up in its own quantitative black box to create two products — Keeper, which tells employers which of their employees are at greatest risk of being recruited away, and Skill Mapper, which provides a summary of the skills possessed by individual workers. LinkedIn allowed HiQ to do this for five years, before developing a very similar product to Skill Mapper, at which point LinkedIn sent the company a “cease and desist” letter, and threatened to invoke the CFAA if HiQ did not stop tapping its user data.

..Meanwhile, a case that might have been significant mainly to digital insiders is being given a huge publicity boost by Harvard professor Laurence Tribe, the country’s pre-eminent constitutional law scholar. He has joined the HiQ defence team because, as he told me, he believes the case is “tremendously important”, not only in terms of setting competitive rules for the digital economy, but in the realm of free speech. According to Prof Tribe, if you accept that the internet is the new town square, and “data is a central type of capital”, then it must be freely available to everyone — and LinkedIn, as a private company, cannot suddenly decide that publicly accessible, Google-searchable data is their private property….(More)”.

Collaborative Platforms as a Governance Strategy

Curated on Posted on by Stefaan Verhulst

Chris Ansell and Alison Gash in the Journal of Public Administration Research and Theory: “Collaborative-Platforms-as-a-Governance-Strategy?redirectedFrom=fulltextCollaborative governance is increasingly viewed as a proactive policy instrument, one in which the strategy of collaboration can be deployed on a larger scale and extended from one local context to another. This article suggests that the concept of collaborative platforms provides useful insights into this strategy of treating collaborative governance as a generic policy instrument. Building on an organization-theoretic approach, collaborative platforms are defined as organizations or programs with dedicated competences and resources for facilitating the creation, adaptation and success of multiple or ongoing collaborative projects or networks. Working between the theoretical literature on platforms and empirical cases of collaborative platforms, the article finds that strategic intermediation and design rules are important for encouraging the positive feedback effects that help collaborative platforms adapt and succeed. Collaborative platforms often promote the scaling-up of collaborative governance by creating modular collaborative units—a strategy of collaborative franchising….(More)”.

How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem

Curated on Posted on by Stefaan Verhulst

Paper by Amanda Levendowski: “As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright.

Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law — the fair use doctrine — has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems….(More)”.

Policy Analytics, Modelling, and Informatics

Curated on Posted on by Stefaan Verhulst

Book edited by J. Ramon Gil-Garcia, Theresa A. Pardo and Luis F. Luna-Reyes: “This book provides a comprehensive approach to the study of policy analytics, modelling and informatics. It includes theories and concepts for understanding tools and techniques used by governments seeking to improve decision making through the use of technology, data, modelling, and other analytics, and provides relevant case studies and practical recommendations. Governments around the world face policy issues that require strategies and solutions using new technologies, new access to data and new analytical tools and techniques such as computer simulation, geographic information systems, and social network analysis for the successful implementation of public policy and government programs. Chapters include cases, concepts, methodologies, theories, experiences, and practical recommendations on data analytics and modelling for public policy and practice, and addresses a diversity of data tools, applied to different policy stages in several contexts, and levels and branches of government. This book will be of interest of researchers, students, and practitioners in e-government, public policy, public administration, policy analytics and policy informatics….(More)”.

Civic Creativity: Role-Playing Games in Deliberative Process

Curated on Posted on by Stefaan Verhulst

Eric Gordon, Jason Haas, and Becky Michelson at the International Journal of Communication: “This article analyzes the use of a role-playing game in a civic planning process. We focus on the qualities of interactions generated through gameplay, specifically the affordances of voluntary play within a “magic circle” of the game, that directly impact participants’ ability to generate new ideas about the community. We present the results of a quasi-experimental study where a role-playing game (RPG) called @Stake is incorporated into participatory budgeting meetings in New York City and compared with meetings that incorporated a trivia game. We provide evidence that the role-playing game, which encourages empathy, is more effective than a game that tests knowledge for generating what we call civic creativity, or an individual’s ability to come up with new ideas. Rapid ideation and social learning nurtured by the game point to a kind of group creativity that fosters social connection and understanding of consequence outside of the game. We conclude with thoughts on future research….(More)”.

The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement

Curated on Posted on by Stefaan Verhulst
Book by Andrew Guthrie Ferguson on “The consequences of big data and algorithm-driven policing and its impact on law enforcement…In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence.
This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information.  The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool.
Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices.  After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias.
But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections.
In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police.  These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime….(More)”